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TITLE

Novice identification of melanoma: not quite as straightforward as the ABCDs.

Authors

R Benjamin Aldridge 1a

Matteo Zanotto 2

Lucia Ballerini 2a

Robert B Fisher 2

Jonathan L Rees 1

1 Department of Dermatology, University of Edinburgh, Lauriston Building, Lauriston

Place, Edinburgh, EH3 9HA

2 School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street,

Edinburgh, EH8 9AB

Funding from The Wellcome Trust (Reference 083928/Z/07/Z) and the Foundation for

Skin Research (Edinburgh) . RBAa and LBa supported by Wellcome Trust.

Corresponding author

Professor Jonathan Rees

Department of Dermatology, University of Edinburgh, Level 1 Lauriston Building,

Lauriston Place, Edinburgh, EH3 9HA

jonathan.rees@ed.ac.uk, Tel: 44-(0) 131 536 2041, Fax: 44-(0) 131 229 8769

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ABSTRACT

The “ABCD” mnemonic to assist non-experts’ diagnosis of melanoma is widely

promoted, however, there are good reasons to be sceptical about public education

strategies based on analytical, rule-based approaches (such as ABCD). Evidence

suggests that accurate skin lesion diagnosis is predominately achieved through non-

analytical pattern recognition (via training examples) and not by rule-based algorithms.

If the ABCD are to function as a useful public education tool they must be used reliably

by untrained novices, with low inter-observer and intra-diagnosis variation, but with

maximal inter-diagnosis differences. The 3 subjective properties (the ABCs of the

ABCD) were investigated experimentally: 33 laypersons scored 40 randomly selected

lesions (10 lesions x 4 diagnoses: Benign naevi, Dysplastic naevi, Melanomas,

Seborrhoeic Keratoses) for the 3 properties on Visual-Analogue-Scales. Our results

(n=3960) suggest that novices cannot use the ABCs to reliably discern benign from

malignant lesions.

Keywords

ABCD, Non-analytical reasoning, Pattern recognition, Skin cancer, Melanoma,

Dermatology diagnosis

Principal Author

Professor Jonathan Rees

Department of Dermatology, University of Edinburgh, Level 1 Lauriston Building,

Lauriston Place, Edinburgh, EH3 9HA

jonathan.rees@ed.ac.uk, Tel: 44-(0) 131 536 2041, Fax: 44-(0) 131 229 8769

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INTRODUCTION

The “ABCD” rule to aid in the diagnosis of early malignant melanoma has recently

celebrated its 25th Anniversary (1). This mnemonic was introduced in 1985 to aid non-

experts’ macroscopic diagnosis of pigmented lesions and over the years it has been

widely promoted in an attempt to facilitate the earlier detection of melanomas (2). In

the twenty-five years since its inception the use of the ABCD has transitioned from

assisting non-expert physicians’ diagnosis, through educating the public in intensive

clinician led programs, to now being used at the heart of most general public education

strategies; with the criteria described on the public websites of the American Academy

of Dermatology (AAD) (3), British Association of Dermatologists (4), Australasian

College of Dermatologists (5) and European Academy of Dermatology and Venereology

(6). Although the fundamental four-part ABCD mnemonic has received widespread

adoption, it is surprising that there has been little apparent validation of its utility as a

general public education strategy.

Given that lay individuals first identify the majority of melanomas and are also

responsible for the largest proportion of the delays in its diagnosis, reliable and

accurate information to assist them in early detection is essential (7-10). Recently the

importance of accurate patient information has been further highlighted as, thus far, the

numerous strategies initiated to improve screening and the early detection of melanoma

have not resulted in a substantial reduction in the proportion of tumours with

prognostically unfavourable thickness (11). There is now mounting evidence leading us

to question the use of the ABCD rule as a general public education strategy (12, 13).

The majority of studies that are cited as providing evidence for the mnemonics’

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adoption have either involved clinician-performed assessments (14-18) or intensive lay

education (19, 20), and, as we detail below, there are methodological limitations with

extrapolating the findings from these studies to general public health promotion - the

roles of experience and prior examples.

The role of experience

Since the late 1980s the cognitive processes involved in dermatological diagnosis have

been under investigation, most notably in the laboratory of Geoff Norman and

colleagues (21-25). At the risk of some simplification, the processes involved in

diagnosis can be viewed as being either explicit and based on conscious analytical

reasoning or, alternatively, as being implicit and holistic, and hidden from the

conscious view of the diagnostician. Dermatologists appear to predominately use the

latter non-analytical reasoning, derived through experience of prior examples to

identify skin lesions. This overall pattern recognition cannot be unlearnt and thus has a

carry-over effect on any attempts at analytical rule application (26-30). In addition,

study designs where experts are asked to explain their diagnoses inherently over-

emphasize algorithmic reasoning by promoting intentional rather than incidental

analytical processing (31). It has even been suggested that experienced clinicians make

a diagnosis intuitively then alter their analytical assessment to fit in with their

preconceptions about the relation between these features (such as the ABCD) and the

diagnosis (such as melanoma) (31, 32). Certainly, the only prospective study of

dermatologists’ recognition patterns, confirmed that whilst analytical pattern

recognition (the ABCD rules) could be used to predict malignancy, it was not actually

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how dermatologists arrived at the correct diagnosis; instead the experts seemed to use

overall or differential pattern recognition (“Ugly duckling sign (33)”) (23).

The role of prior examples

The exact number of prior examples that are required to significantly alter analytical

assessments is unknown, but we do know that these carry-over effects do not only

apply to seasoned clinicians; novices have been shown to exhibit this bias with only a

few prior examples (27, 28, 34). We do not fully understand how novices naturally

visually assess skin lesions, but unless it is significantly different to the rest of human

visual perception it is unlikely to be by analytical methods. If, as is likely, overall-

pattern recognition plays even a small role, the biasing effect of prior examples needs

to be controlled for when assessing the analytical ABCD criteria. In the few studies

where intensive ABCD education has been demonstrated to have a beneficial effect on

lay individuals, overall pattern recognition was not controlled for (19, 20). It is therefore

unclear how much of the positive effect can be attributed to the novices’ ability to

discriminate the true analytical ABCD criteria rather than their ability to use overall-

pattern recognition ‘learnt’ from the example lesions used to demonstrate the analytical

criteria during their patient education. Thus far the only randomized control trial testing

the ABCD in lay hands showed that it decreased diagnostic accuracy and was not as

effective as a pattern recognition education strategy (12).

In this particular context any screening or diagnostic tool would ideally have the

following three criteria: Inter-observer variability should be minimal; variation within a

diagnostic class should be small; and the inter-diagnosis differences should be

significant. In the present study we set out examine these three criteria experimentally

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assessing the three subjective analytical properties (the ABCs) of the ABCD rule.

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METHODS

Study Image selection

40 digital images of pigmented skin lesions were randomly selected from 878 relevant

images in the University of Edinburgh Department of Dermatology’s image database.

The Department's image library (now comprising over 3500 images) has been

prospectively collected for ongoing Dermato-informatics research investigating semi-

automated diagnostic systems and non-expert education. The 40 images selected for

this experiment were stratified so that there were 10 images from each of the following

4 diagnostic classes: benign naevi, dysplastic naevi (defined as lesions with histological

atypia), melanomas and seborrheic keratoses. All the images had been collected using

the same controlled fixed distance photographic setup; Canon (Canon, Japan) EOS

350D 8.1MP cameras, Sigma (SIGMA, Japan) 70mm f2.8 macro lens and Sigma EM-

140 DG Ring Flash at a distance of 50cms. The lesions were cropped from the original

digital photograph, each to the same resolution (600x450 pixels) and displayed in a 1:1

ratio (approximately equivalent to 7 times magnification from the original 50cm

capturing distance) in the custom built experimental software.

Software design & implementation

A purpose built program was created to allow the 3 subjective criteria of the ABCD rule

(Asymmetry, Border Irregularity and Colour Uniformity) to be tested remotely over the

Internet, to limit any investigator-related interaction bias. Diameter was felt not to be

suitable for assessment as the images were magnified and thus would have required the

placement of a relative 6mm marking scale next to each lesion before asking each

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participant to comment which was longer; the lesion or the measuring scale, which

would not have been instructive. The program was written in JavaScript and PHP, and

after entering their demographics the participants were given a set of instructions stating

how to use the ABC criteria and the software. The instructions were based on the verbal

descriptors of the ABC criteria taken from the website of the AAD (3). After confirming

that they had read and understood the instructions the subjects assessed each of the 40

images in turn for Asymmetry, Border Irregularity and Colour Uniformity on a 10-point

visual analogue scale (VAS). A screen shot of the software can be seen in Figure 1. At

any stage the subjects could click on a “Help” button to redisplay the instructions and

verbal descriptors of the ABC.

To minimize any lead bias or fatigue effect, the software was programmed to

randomize the order in which the subjects undertook the 40-lesion assessment, so that

it was different for each participant. In addition to the verbal descriptions of the 3

properties assessed, to further reduce inter-rater differences visual anchor points were

integrated into the software at the mid point and either ends of the rating scale. These

anchor points were taken from the ABCDE patient guidelines on the SkinCancerNet

“Melanoma: What it Looks Like” webpage produced in conjunction with the AAD (35).

As we were interested in the lay public’s ability to discriminate analytically the three

properties and not their ability to use their innate non-analytical reasoning to match or

distinguish from real-life examples, only the caricatured diagrams from the

SkinCancerNet website were used as the high-end anchor points, rather than example

pictures of melanomas (Figure 2). The mid and low-end anchor points were computer

generated to complete the VAS.

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Subjects

An open email request containing the URL link to the study was distributed to MSc

students of the University of Edinburgh’s School of Informatics, inviting them to

personally undertake the study and forward on to non-medical acquaintances who also

might be willing to participate. 33 lay subjects agreed to participate without

remuneration. Sex distribution was split with 21 males and 12 females (64% male).

Mean age was 34 years old (Range 17-62). None of the subjects had a personal history

of skin cancer.

Statistics

The subjects’ responses were automatically recorded by the program then exported into

‘R for MacOS’ for graphing and statistical analysis (36).

Ethics

NHS Lothian research ethics committee granted permission for the collection and use

of the images, and additional permission for the use of students in this research was

granted through the University of Edinburgh’s “Committee for the use of student

volunteers”.

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RESULTS

The full results of all 3960 analytical VAS scores attributed in the study (33 subjects x

40 lesions x 3 ‘ABC’ properties) are graphically displayed in Figures 3a-c, with each

property presented in a separate plot. At first glance these plots may seem complicated

but they have the virtue that every data-point from the experiment is presented and the

variability in scoring can be instinctively assessed. In explanation: each horizontal

coloured bar represents an individual subject’s score for a specific lesion, with each of

the 40 lesions displayed in individual columns across the X-axis. These 40 lesions’

columns are grouped into different colours according to their diagnostic classes

(Green= the 10 Benign Naevi, Orange= the 10 Dysplastic Naevi, Red= the 10

Melanomas, Blue= the 10 Seborrheic Keratoses). The overall median score for each

diagnostic class is demonstrated by the large horizontal black bar, straddling each of

the 4 coloured series of 10 columns.

The inter-person variability in assessing each of the 3 ABC properties for any single

lesion is represented by a single column’s vertical spread across the Y-axis. Within a

specific diagnostic class the variability in scores is demonstrated by the differences in

vertical spread between the 10 uniform coloured columns. The variability between the

4 diagnostic classes is appreciated by the differences in the overall distributions

between the 4 coloured groups and further enforced by the differences in their median

scores indicated by the horizontal black bars.

The results are further summarised in Table 1.

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Whilst it is possible to appreciate the small, albeit significant (Kruskal

Wallis=P<0.0001), difference between the 4 diagnostic groups’ scores what is far more

striking is the substantial spread in the scores attributed to the same lesion by the 33

subjects and the further variation in scoring between the 10 lesions within the same

diagnostic class for all 3 of the subjective ABC properties. Additional data analysis

demonstrates that a similar substantial variation exists within the 10 scores that each

individual attributed to the lesions within the same diagnostic class (data not shown).

The inter-person and intra-class variations can be better appreciated by specific

examples (see Figure 4, which is an enlarged view of the highlighted section of Figure

3b). In Figure 4 it can be seen that lesion 3 (blue arrow/highlights) which had the

largest inter-person variation (IQR= 4.86) of the 10 lesions within the melanoma class

had a range of ‘border irregularity’ scores attributed by the 33 subjects from 0.7 to 10

with a median score of 6.6, and lesion 7 (cyan arrow/highlights), which had the least

inter-person variance (IQR=1.93), had a range from 0 to 5.5 with a median score of

1.3.

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DISCUSSION

Our principal motivation for the current work was the observation that the original

rationale and justification for the ABCD approach had slipped from the primary target

group of physicians to the lay public, with little supporting evidence to justify this

transfer. In the absence of empirical evidence of effectiveness, there are at least two

theoretical reasons to be suspicious of this approach. First, there is an increasing body

of evidence that experts are not necessarily able to explicitly state the basis of their own

expertise in a way that is simply transferable (31, 32). Second, that previous testing of

ABCD had not controlled for prior exposure (14-20), meaning that prior accounts of the

utility of the ABCD may have reflected prior experience and knowledge rather than the

implementation and use of the criteria themselves (26-30).

Other relevant considerations are that whereas experts may be able to use the criteria

on appropriate subclasses of lesions (i.e. melanocytic nevi and melanomas), available

evidence suggests that distinguishing primary melanocytic lesions from mimics (such as

seborrhoeic keratoses) requires considerable expertise (37-39). Finally, the only large

scale RCT in this area comparing ABCD approach with those based on pattern

recognition provided little support for the use of the ABCD criteria (12).

The approach we took was an experimental one attempting to delineate the

characteristics of the ABCD rules on a test series of lesions. Our rationale was that for

the ABCD system to be capable of guiding diagnosis, it would have to have certain

statistical properties: different diagnostic groups needed to score differently, and

variance between persons for the same lesion and within diagnostic groups needed to

be small. Within the limits of our test situation and the images randomly chosen it is

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self evident that these criteria were not met. Different people judged the same lesion

very differently, and although the medians of different diagnostic groups differed, the

overlap was considerable. Looking at Figure 3, it is difficult to imagine being able to

choose any criteria based on ABCs that would usefully discriminate suspicious from

banal lesions.

There are limitations to our approach. Our subjects were not chosen at random, and

were highly educated, computer literate, and likely above average at abstract and

analytical reasoning. We doubt that this is a reason to doubt the generalisability of our

conclusions. Second, we included not just primary melanocytic lesions but mimics

such as seborrhoeic keratoses. The justification for this is simply that non-experts and

many physicians are not able to reliably distinguish between these classes of lesions. In

practice the ABCD criteria get applied (incorrectly) to various diagnostic classes: we

needed to account for this. Third, as the subjects undertook the experimental task

remotely over the Internet we were unable to assess the ‘effort’ they applied to their

scoring. However, because we randomized the order in which each subject assessed

the 40 lesions any fatigue effect would have been minimized. Indeed, close inspection

of the data demonstrates that whilst there is substantial overlap in scoring between (and

within) the diagnostic groups, the subjects’ scoring was not random; individual lesions

each had (to varying degrees) distinct scoring patterns.

We cannot say whether, if subjects had undergone intense education in the use of the

ABCD approach, the results might have been different. In practice, however, the

promulgation of the ABCD criteria via web sites and patient leaflets provides little

opportunity for such intense education. We also suggest that previous studies of the

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ABCD approach have been methodologically compromised because of failure to

control for prior exposure during the teaching phase. Training persons in the use of the

ABCD inevitably means exposure to test images: during this, albeit minimal exposure,

pattern recognition skills will develop, and it is a mistake to believe that any change in

performance pre and post test is due to the ABCD system rather than other learning. To

make any other conclusion requires a degree of experimental control that has been

lacking in prior work.

We also acknowledge the multiple modifications to the basic ABCD mnemonic that

have been suggested over the years to “improve” its functionality (40-47), and accept it

is now commonplace to use the ABCDE criteria (although we note there is a wide

variety of ‘E’s suggested; evolving, enlarging, elevated, erythema, expert). However, in

light of the fact that there is now further evidence that untrained novices cannot use the

analytical ABC criteria effectively, should the public education message not be

simplified to only include Evolving (i.e. Change). Such a simplification has previously

been suggested by Weinstock (47, 48) and has independently been found to be the

most important predictor of melanoma in patient observed features (13).

Finally, given the changing epidemiology of malignant melanoma and the importance

of early presentation by patients and early diagnosis by physicians, our criticisms of the

ABCD approach are not meant to disparage attempts to develop alternative strategies.

In this respect we note the work of Grob who has used approaches based on fostering

pattern recognition skills for laypersons (12). Our own work has also suggested that use

of images and a structured database may enhance diagnostic skills of laypersons,

although in the context of malignant melanoma, such systems need far more testing

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before being promoted as being clinically useful (49, 50).

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ACKNOWLEDGMENTS

The work was supported by The Wellcome Trust (Reference 083928/Z/07/Z) and the

Foundation for Skin Research (Edinburgh). We are also grateful to the advice and

assistance given by Karen Roberston and Yvonne Bisset (Department of Dermatology,

University of Edinburgh) regarding the photographic capture and preparation of the

digital images. We also recognize the contribution of Nikolaos Laskaris (School of

Informatics, University of Edinburgh) who undertook some of the preliminary

programming as part of his MSc Thesis (51).

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FIGURES

Figure 1 Legend

A screen “snap shot” of the purpose built software used to record the 33 subjects’

assessments of the 3 ABC properties. The subjects scored each of the 3 properties on

the 10-point Visual Analogue Scales that were displayed to the right of the image, by

moving the slider to the desired level.

Figure 1

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Figure 2 Legend

A screen “snap-shot” taken from the SkinCancerNet website (35), demonstrating the

caricatured images which we used as the anchor points for the Visual Analogue Scales

in our software. The pictures on the right were used as they demonstrate the analytical

criteria of the ABC, but without facilitating any non-analytical pattern recognition that

could have developed if the “real-life” images had been used.

Figure 2

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Figure 3 Legend

The full results of all 3960 comparisons undertaken are split according the ABC

properties assessed into 3 plots (Figure 3a- Asymmetry; 3b-Border Irregularity; 3c-

Colour Uniformity). Each horizontal bar represents an individual’s score. The 40 lesions

assessed are displayed in columns across the X-axis, grouped by their diagnostic classes

(Green= Benign Naevi, Orange=Dysplastic Naevi, Red=Melanomas, Blue=Seborrheic

Keratoses). The median score for each diagnostic class is demonstrated by the large

horizontal black bar.

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Figure 4 Legend

An enlarged display of the highlighted section of Figure 3b, showing all the Border

Irregularity VAS scores for the 10 Melanoma lesions. The lesions with the highest

variation (lesion 3, IQR=4.86) and lowest variation (lesion 7, IQR=1.93) are further

highlighted, in blue and cyan respectively, to demonstrate the large spread of scores

attributed to lesions within the same diagnostic class. For lesion 3 it can be seen that

the range of scores attributed by the 33 subjects was 0.7 to 10 with a median of 6.6 and

for lesion 7 the range was 0 to 5.5 with a median of 1.3.

Figure 4

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Table 1:

Lesion  Class 

Benign  Naevi 

Dysplastic Naevi 

Melanomas  Seborrheic Keratoses 

Asymmetry VAS Scores (0= Symmetric, 10=Asymmetric) 

Median  3.66  4.55  4.83  4.77 

IQR  4.96  4.88  5.94  4.07 

90th Percentile  8.93  8.97  9.52  8.09 

Border Irregularity VAS Scores (0= Regular, 10=Irregular) 

Median  2.37  3.77  3.68  2.72 

IQR  4.61  5.05  6.46  4.08 

90th Percentile  8.27  8.93  9.78  7.52 

Colour Uniformity VAS Scores (0= Single uniform colour, 10= Multiple or non‐uniform colour distribution) 

Median  3.92  4.83  5.63  4.92 

IQR  5.00  4.88  5.59  4.23 

90th Percentile  8.26  9  9.59  8.34